Tootfinder

Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Subhajit Maity, Ayan Kumar Bhunia, Subhadeep Koley, Pinaki Nath Chowdhury, Aneeshan Sain, Yi-Zhe Song
https://arxiv.org/abs/2507.07994

Doodle Your Keypoints: Sketch-Based Few-Shot Keypoint Detection
Keypoint detection, integral to modern machine perception, faces challenges in few-shot learning, particularly when source data from the same distribution as the query is unavailable. This gap is addressed by leveraging sketches, a popular form of human expression, providing a source-free alternative. However, challenges arise in mastering cross-modal embeddings and handling user-specific sketch styles. Our proposed framework overcomes these hurdles with a prototypical setup, combined with a gr…

Learning Japanese with Jouzu: Interaction Outcomes with Stylized Dialogue Fictional Agents
Zackary Rackauckas, Julia Hirschberg
https://arxiv.org/abs/2507.06483

Learning Japanese with Jouzu: Interaction Outcomes with Stylized Dialogue Fictional Agents
This study investigates how stylized, voiced agents shape user interaction in a multimodal language learning environment. We conducted a mixed-methods evaluation of 54 participants interacting with anime-inspired characters powered by large language models and expressive text-to-speech synthesis. These agents responded in Japanese character language, offering users asynchronous, semi-structured conversation in varying speech styles and emotional tones. We analyzed user engagement patterns, perc…

Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits
Raj Mahmud, Shlomo Berkovsky, Mukesh Prasad, A. Baki Kocaballi
https://arxiv.org/abs/2508.02328

Understanding User Preferences for Interaction Styles in Conversational Recommender Systems: The Predictive Role of System Qualities, User Experience, and Traits
Conversational Recommender Systems (CRSs) deliver personalised recommendations through multi-turn natural language dialogue and increasingly support both task-oriented and exploratory interactions. Yet, the factors shaping user interaction preferences remain underexplored. In this within-subjects study (\(N = 139\)), participants experienced two scripted CRS dialogues, rated their experiences, and indicated the importance of eight system qualities. Logistic regression revealed that preference f…

Evaluating the Effectiveness of Large Language Models in Solving Simple Programming Tasks: A User-Centered Study
Kai Deng
https://arxiv.org/abs/2507.04043 …

Evaluating the Effectiveness of Large Language Models in Solving Simple Programming Tasks: A User-Centered Study
As large language models (LLMs) become more common in educational tools and programming environments, questions arise about how these systems should interact with users. This study investigates how different interaction styles with ChatGPT-4o (passive, proactive, and collaborative) affect user performance on simple programming tasks. I conducted a within-subjects experiment where fifteen high school students participated, completing three problems under three distinct versions of the model. Eac…

ChoreoMuse: Robust Music-to-Dance Video Generation with Style Transfer and Beat-Adherent Motion
Xuanchen Wang, Heng Wang, Weidong Cai
https://arxiv.org/abs/2507.19836 https://…

ChoreoMuse: Robust Music-to-Dance Video Generation with Style Transfer and Beat-Adherent Motion
Modern artistic productions increasingly demand automated choreography generation that adapts to diverse musical styles and individual dancer characteristics. Existing approaches often fail to produce high-quality dance videos that harmonize with both musical rhythm and user-defined choreography styles, limiting their applicability in real-world creative contexts. To address this gap, we introduce ChoreoMuse, a diffusion-based framework that uses SMPL format parameters and their variation versi…

Research on Conversational Recommender System Considering Consumer Types
Yaying Luo, Hui Fang, Zhu Sun
https://arxiv.org/abs/2508.13209 https://arxiv.org/p…

Research on Conversational Recommender System Considering Consumer Types
Conversational Recommender Systems (CRS) provide personalized services through multi-turn interactions, yet most existing methods overlook users' heterogeneous decision-making styles and knowledge levels, which constrains both accuracy and efficiency. To address this gap, we propose CT-CRS (Consumer Type-Enhanced Conversational Recommender System), a framework that integrates consumer type modeling into dialogue recommendation. Based on consumer type theory, we define four user categories--depe…